Processing natural language arguments and propositions

ABSTRACT

Natural language content can be provided by multiple and various sources. Once in text format, such content may be provided to various systems for further processing to identify one or more propositions. The relationship between each proposition may be identified and ordered according to the identified relationships. A visual display may be generated to illustrate the identified propositions and relationship, as well as identify any propositions that may be missing, unsupported, or other characteristic thereof.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present patent application claims the priority benefit of U.S.provisional patent application No. 62/645,114 filed Mar. 19, 2018, thedisclosure of which is incorporated by reference herein.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present invention relates to processing natural language content toidentify semantic components of argumentation and rhetoric.

2. Description of the Related Art

Humans constantly engage in persuasive discourse across various media ofinteraction. It is often the case that parties engaged in persuasivediscourse are unaware of the internal motivations of other partiesparticipating in the discourse. In many cases, a party may not even beentirely aware of their own internal motivations. Such lack of awarenessof baseline motivations may cause participants to “talk past eachother,” which may thus greatly reduce the efficiency of communication.Further, as discourse unfolds and iterates, this “talking past eachother” can cause incorrect or misleading information to be entered intothe conversation, which may thus cause further confusion and “noise” inthe sphere of the discourse and which may be exacerbated in where thediscourse is a public discourse.

Furthermore, parties often put forth arguments that are not fully ordirectly supported by the claimed premises and logic, eitherintentionally or accidentally. Arguments may also hold up to light orsurface level scrutiny but may still fail upon a thorough and tediousinspection of the validity of the supporting propositions or premises.However, even when incentivized, individual community members must spendconsiderable time manually reviewing support for arguments, or lackthereof, and this review is often prone to errors, inconsistencies, andincompleteness.

There is, therefore, a need in the art for improved systems and methodsof processing natural language arguments and propositions.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a system diagram illustrating a system for processing naturallanguage content in accordance with various embodiments of the subjecttechnology;

FIG. 2 is a system diagram illustrating a system for processing naturallanguage content in accordance with various embodiments of the subjecttechnology;

FIG. 3 is a flowchart illustrating a method for processing content inaccordance with various embodiments of the subject technology;

FIG. 4 is a system diagram illustrating a system for processing naturallanguage content in accordance with various embodiments of the subjecttechnology;

FIG. 5 is a flowchart illustrating a method for identifying conflictingpropositions in accordance with various embodiments of the subjecttechnology;

FIG. 6 is a flowchart illustrating a method for identifying componentsof persuasive leverage in accordance with various embodiments of thesubject technology;

FIG. 7 is an illustration of an interface depicting a neutral languageproposition taxonomy in accordance with various embodiments of thesubject technology;

FIG. 8 is an illustration of an interface depicting argument tags inaccordance with various embodiments of the subject technology;

FIG. 9 is an illustration of an interface depicting an argument form inaccordance with various embodiments of the subject technology;

FIG. 10 is an illustration of an interface depicting a proposition flowtree in accordance with various embodiments of the subject technology;and

FIG. 11 is a system diagram of an example computing system that mayimplement various systems and methods discussed herein in accordancewith various embodiments of the subject technology.

DETAILED DESCRIPTION

Aspects of the present disclosure involve systems and methods forextracting information of multiple levels of abstraction andinformational distance from natural language. The extracted informationcan then be reviewed and verified by users, be used to train artificialintelligence (AI) systems (e.g., involving machine learning modelsand/or rules-based learning), and be incorporated into a user interfacefor display.

Natural language content can be provided by multiple and varioussources. For example, natural language content may be provided in anaudio format (e.g., over a broadcast, stream, or recording) and thenprocessed into a text format, which may provided to various systems forfurther processing as described herein. In other examples, a user maydirectly provide natural language text to a user device (e.g., throughan input device, widget, other browser utility, or as a text document).

Further, a user may provide to the system the natural language contentof a third party in order to review or map the semantic content of thethird party's natural language content. For example, a participant of adebate on an online discussion forum can highlight a selection of textincluded in a post by another participant and by using a tool such as abrowser widget, may map the argumentation of the post. Argumentation mayinclude propositions, chain of reasoning, logical elements,sub-propositions, and proponent motivations, which may be implicitly orexplicitly included in a party's statements. The mapped argument may bedisplayed to the user, and the user may provide additional propositionsto explore hypothetical responses to the expanded mapped argument.Further, the chain of reasoning can be displayed for the user, so thatsub-propositions that are inherent to the truthfulness of a propositioncan be identified, reviewed, and similarly explored (e.g.,sub-propositions of sub-propositions and so on). In other words, a stackof propositions underlying a stated proposition can be revealed.

Arguments may be mapped using ontologies related to various domains inorder to efficiently tag arguments, organize propositions, determinerules related to the relationship between propositions, and determinerules for resolving arguments. The domains can include generalized“problem spaces” (e.g., abstracted concepts related to groupings ofproblem types), professional specialties, syntactical elements,reasoning or logic elements, and the like. Each ontology may include ahierarchical definition of entities and/or objects and theirrelationships within the particular domain space. Ontologies can beprobabilistically determined, defined by a rules-based process, or a mixof the two.

For example, and without imputing limitation, words from a naturallanguage content may be mapped along a multi-dimensional vector, as wellas based on a coordinate value within a particular semantic space. Eachdimension or axis can then be related to a particular abstraction (suchas ownership, occupation, field of work, etc.), which may be used toplace the mapped word or words into a mapped space. Clustering, distancebetween words, topographies, topologies, and the like can be used toidentify particular groupings and to identify a word or words as relatedto particular concepts. In other examples, ontologies can be definedusing specified rules, such as keyword or word grouping identification.If a particular word or sequence of words is observed (e.g., via stringmatching and the like), the word, sequence of words, segment of text, ornatural language content in which they were identified can be taggedaccording to a predetermined mapping.

The ontologies may further be used to define relationships betweenpropositions and arguments, so that the arguments and relationships maybe placed within an idea and/or argument map (“IAM”). The IAM can beused to further process the natural language content, provide avisualization of the flow of ideas and/or arguments contained within thenatural language content, or perform simulations of various hypotheticalresponses or expansions of the contained ideas or arguments. In someexamples, a user may define concepts and relations for mapping. The usermay then be presented with suggestions for further concept and relationdefinitions (e.g., based on a model informed by historical selections,the user, and/or a global history). Further, the informed model may beupdated in response to the how the user utilizes the suggestions.

An argument analysis tool can further use the IAM to provide insightsbased on the mapped propositions and/or sentences using one or morerules. Demonstrative arguments may be analyzed to identify and verifyexplicit propositions, as well as implicit or necessary sub-propositionsthat may support or invalidate the explicit propositions of the analyzedargument. Analysis may be provided by a first-order logic engine thatexamines the truth of propositions or may be performed by a higher-orderor non-classical logic engine that processes and analyzes therelationships between propositions and relationships betweenrelationships (e.g., interdependency between two particular propositionsmay be reliant upon another interdependency between two otherpropositions). In some examples, graph-based analysis may identifyvarious characteristics of an argument (e.g., circular reasoning asindicated by cycles and the like). In other examples, Bayesian and/orMarkov-based probabilistic analysis may place argument components, andcomplete arguments along a spectrum of likelihood based on identifiedprobabilities of explicit and implicit propositions.

A Neutral Language Proposition Taxonomy (“NLPT”) may include a taxonomyin the form of a tree data structure where each node of the treerepresents a neutral language proposition or topic in increasingdefinition or specificity. In some examples, the NLPT can be displayedas embedded hashtags or hyperlinks interconnecting nodes. The NLPT canuse the ontologies to identify propositions within a natural languagecontent input provided directly by an author of the content or by athird party reviewing the content. The NLPT may be used to associatepropositions in a set of natural language content with comparableemotionally-neutral propositions from within the taxonomy. In otherwords, the NLPT can be used to neutralize the rhetoric of argumentation(e.g., by removing invectives, judgmental adverbs, exaggeration, and thelike), which may otherwise obfuscate the propositional content of astatement with emotive qualifiers unrelated to the truth value of theproposition under consideration. Such neutralization may be achieved byassociating the natural language content with a selection of moreneutral propositions expressing a similar thought.

In some examples, the NLPT can operate in real-time, continuouslyprocessing text as it is written (e.g., via a browser widget, wordprocessor plug-in, individually executable software, and the like).Having identified a proposition, the NLPT may then identify a nodewithin the taxonomy tree of the NLPT that corresponds to a similarproposition. In some examples, the NLPT may prompt the user to verifythat the identified topic is correct, or the user may provide their owntopic, which may then be inserted into the tree nearby the nodes (e.g.,topics) identified by the NLPT. This way, the tree may expand and/orincrease in accuracy, while simultaneously providing a taxonomicoverview of natural language content input to users. Further, the NLPTmay group non-neutral language under comparable neutral language nodes,thus providing navigation between similarly-grouped propositionalcontent and insights into community trends in a manner similar to thatof “hashtags” and other such concepts. In some examples, such promptingfunctions may be performed before the drafter has published to theaudience or as part of a review performed by users or the system afterpublication.

Having identified nodes within the tree based on the natural languagecontent input, a system including the NLPT may prompt a user to identifyarguments being made in relation to the propositions. In some examples,the IAM can identify the arguments and ask a user to validate theidentified arguments. The IAM may improve its argument identificationbased on user validation or lack thereof, which may further be used torefine future performance of argument identification. Argument types mayinclude, without imputing limitation, fact being contradicted, argumentby analogy, category mismatch, principle being violated, solution beingproposed, consequence being asserted (and a normative evaluation of thatconsequence), outcome different from intent, topic shift or splitting,non-sequitur, questions of degree, confusion of subject agency or theaffected parties, assignment of burden of proof, question begging (e.g.,using a premise to support itself or circular reasoning), contradiction,and incompleteness of a set.

In some examples, the system may prompt a user to provide furtherelements and propositions to complete an otherwise incomplete argumentbased on the identified arguments. For example, where a natural languagetext input is identified as an argument by analogy, the system mayprocess the subjects being analogized and provide the user a listing ofsimilarities and dissimilarities between the analogized subjects. Suchanalysis may make use of various databases and information sourcesavailable over a communication network. Where different informationsources may provide different information, some sources may beprioritized based on such factors as history of validation, preference,timeframe of the information, direct/indirect sourcing, etc.

The NLPT can also identify proposition interrelationships for the userby providing a sequence of identified propositions. The sequence may bea representation of the identified nodes of the taxonomy tree. Arelative placement of individual propositions may further be provided bythe NLPT. For example, a dropdown list of a hierarchy of propositionscan be provided to the user where each entry on the list representsrelated propositions (e.g., within a particular traversal distance ordirection of the identified proposition). The proposition identified bythe NLPT may be highlighted or otherwise marked within the dropdownlist. In some examples, the user may select nearby propositions, and thesystem may identify differentiating elements (e.g., a route oftraversal) between the selected proposition and the currently-identifiedproposition. Further, elements may be recommended to traverse thedifference and thus assist the user in providing a more robust supportfor their intended argument or to identify gaps between an opponent'sstated position and the propositions put forth in support of thatposition.

Hyperbolic or incendiary components of an entered proposition may beremoved from the natural language content upon associating the contentwith a node of the NLPT and that node's associated neutral language(e.g., as compared to the language provided by the user). Hyperbolic orincendiary components can be identified by, for example, a sentimentanalysis engine or the like (not depicted). As language is drafted, thesystem may identify and highlight hyperbolic or incendiary componentsfor the drafter in order to allow the drafter to minimize (or, ifpreferred, maximize) the emotive elements of an argument.

FIG. 1 depicts a system 100 for processing natural language content 102to provide an argumentation and proposition analysis output to a user112 (by way of a user device). The user 112 can view the output througha user interface, which may be provided in a computer, mobile device,smartphone, laptop, and the like. In some examples, the user 112 caninteract directly with the system 100 through the user interface and mayprovide the natural language content 102 as text, audio, or video data.

The natural language content 102 is first received by an ontologicalclassifier 104. The ontological classifier 104 can includedomain-specific ontologies related to professional domains, socialdomains, political domains, cultural domains, and the like. In someexample, the ontological classifier 104 may be one or more trainedmodels. In other examples, the ontological classifier 104 can be one ormore rules-based systems or a mixed system for identity-specificontologies and/or ontology types. Further, the ontological classifier104 can include overlapping domain ontologies 105A, joint domainontologies 105B, or domain ontologies of varying sizes 105C-D. Theontological classifier 104 processes the natural language content 102and provide abstracted conceptual information, such as propositions andarguments that are associated with various portions of the naturallanguage content 102. Different portions of the natural language content102 can therefore be tagged and provided as tagged natural languagecontent 103 (tagged to identify portions of abstracted conceptualinformation), before being provided to a neutral language propositiontaxonomy 106 and an idea argumentation mapper 108.

The neutral language proposition taxonomy 106 may include a hierarchicalorganization of propositions. Here, the neutral language propositiontaxonomy 106 includes a taxonomy tree 107. In some examples, the neutrallanguage proposition taxonomy 106 can instead or also include othergraph types, lists, semantic spaces, and the like. In some examples, theneutral language proposition taxonomy 106 can be domain-specific and mayvary based on which domain-specific ontology of the ontologicalclassifier 104 used to process the natural language content. The taggednatural language content 103 can be mapped within the neutral languageproposition taxonomy 106 (e.g., to the taxonomy tree 107) in order toidentify nearby propositions, as well as identify distance betweenidentified propositions and unidentified propositions that may belocated between identified propositions within the propositionhierarchy.

The idea argumentation mapper 108 can use the tagged natural languagecontent 103 to generate a mapping of the tagged natural language content103 representing arguments contained within it. Further, the ideaargumentation mapper 108 may receive taxonomy information from theneutral language proposition taxonomy 106 to identify arguments and insome examples, strength of the arguments based on identifiedpropositions and relationships between the propositions (e.g., distanceor intervening propositions and the like).

Distances, intervening propositions, depth of a proposition within theneutral language proposition taxonomy 106, and other information—alongwith the mapping generated by the idea argumentation mapper 108—can beprovided to an argumentation analytics engine 110. The argumentationanalytics engine 110 can receive feedback from the user 112, which mayinclude informing or validating the output of the neutral languageproposition taxonomy 106 or the idea argumentation mapper 108. In someexamples, the user 112 can provide hypothetical responses to theanalytic engine 110 in order to explore potential argument rebuttals orsupports. Similarly, the argumentation analytics engine 110 may useinput from the user 112 to update the map generated by the ideaargumentation mapper 108, the neutral language proposition taxonomy 106,or the ontological classifier 104.

FIG. 2 illustrates a content processing system 200, and FIG. 3illustrates a content processing method 300. The content processingsystem 200 can receive natural language content in various forms, whichmay be processed differently. Source content can include, withoutlimitation, a “simple comment,” a “compound comment,” an “onlinearticle,” “print,” “recorded speech,” and “live speech.” Where thesubmitted natural language content is in non-text form (e.g., in thecase of recorded speech or images), such content may first be processedby a speech-to-text or optical character recognition (“OCR”) system (notdepicted) in order to provide text data. In some examples, preprocesseddata may include metadata associated with various contextual elements ofthe natural language content, such as speaker posture, vocal intonation,surrounding environment, and the like.

A natural language text interface 202 receives the natural languagecontent (operation 302). The natural language text interface 202 can bea “drag-and-drop” interface, which receives a text file, a browserwidget, or a separate software process running, for example, as abackground operation. The natural language text interface 202 mayprovide the natural language content to a neutral language propositiontagger 204. Referenced content 203 may also or instead be provided tothe neutral language proposition tagger 204. For example, a hyperlinkcan be provided to the system 200 in lieu of a direct text document, andthe system may process the natural language content of the referencedcontent 203.

The neutral language proposition tagger 204 can identify propositionswithin the received natural language content and tag them with neutralproposition values (operation 304). In some examples, when the naturallanguage content is a simple comment or compound comment, a propositionand propositions may respectively be tagged to the entirety of thesimple comment or compound comment. Where the natural language contentis provided via referenced content 203 (e.g., an online article, print,recorded speech, or live speech), the neutral language propositiontagger 204 may tag the content with inline propositions throughout thenatural language content.

Having tagged the natural language content, the neutral languageproposition tagger 204 can send the tagged natural language content toan argument type tagger 206. The argument type tagger 206 may tagelements of the natural language content with argument types (operation306). In some examples, such as when the system 200 receives an image asreferenced data 203, the natural language content may be passed withoutproposition tags and may only tag the image content with an argumenttype.

The argument type tagger 206 may prompt a user 210 (through a userinterface 208), a system curator 212, or a community 214 for inputsspecifying argument forms of the natural language content (operation308). Argument forms may be arguments intended by the user and so theuser may verify the application of their intended argument by inputtingit to the type tagger. In some examples, the system curator 212 orcommunity 214 may provide argument forms observed in the naturallanguage content. Multiple argument forms can be provided, and theargument type tagger 206 can further tag elements of the naturallanguage content with argument types as new forms are provided andbefore providing the fully tagged natural language content to aproposition organizer 216. In some examples, users 210, system curators212, or communities 214 can credential or otherwise authorize anotherone or more other users 210, system curators 212, or other communities214 to interact with the system 200 on their behalf. Communities 214 canuse various systems, such as community member voting to authorize orcredential other communities 214, system curators 212, or users 210. Forexample, a first user 210 may authorize a colleague (e.g., another user210) to provide propositions in support of or contrary to argumentsprovided by the first user. Community-sourced inspection (e.g., bycredentialed entities) may be incentivized to provide increased scrutinyto topics, for example as disclosed by U.S. Pub. No. 2016/0148159 whichis hereby incorporated by reference in its entirety.

As depicted in FIG. 8, the user interface 208 can display the argumentform or forms identified by the argument type tagger 206 via a userdisplay 800. The user display 800 can include a set of natural languagecontent 802 and an associated argument form 806 among a list ofarguments 804. In some examples, different arguments can be selected orprovided entirely by a user.

Returning to FIG. 2, the proposition organizer 216 organizespropositions based on the argument and neutral language proposition tagsinto argument abstractions (operation 310). Depending on the type ofsource content, the proposition organizer 216 may generate differentargument abstractions. In the case of a simple comment, a singleproposition may be identified, the argument abstraction may be thetagged proposition, and in some cases, related propositions that are notexplicitly included with the natural language content may also beidentified. A compound comment may result in an argument abstractionincluding multiple propositions. Online articles, print, recordedspeeches, and live speeches may result in argument abstractionsincluding proposition maps that may include interconnected propositions.

A proposition interrelationship identifier 218 may identifyinterrelationships between the organized propositions provided by theproposition organizer 216 (operation 312). Propositioninterrelationships may describe how propositions support or refute eachother. In some cases, the users 210, the system curator 212, or thecommunity 214 can interact with the propositions via the user interface208 (operation 314). As proposition interactions are received, theproposition interrelationship identifier 218 can update howinterrelationships are identified or record new interrelationshipsbefore receiving further proposition interactions. In this way, multipleusers can interact with propositions in an effort to support or refute aproposition identified in a natural language input.

A conclusion tabulator 220 can tabulate a conclusion based on theorganized propositions, interrelationships, and proposition interactions(operation 316). The conclusion tabulator 220 can provide requested orreal-time updates to users via the user interface 208. For example,where multiple users 210 or a community of users 214 interact with theproposition interrelationship identifier 218, a running conclusiontabulation may be provided over the user interface 208 so users can seethe success or failure of their interactions in real-time. In someexamples, the conclusion tabulator 220 traverses a chain of reasoning totabulate conclusions of sub-propositions as well. This traversal can beiterative, recursive, or otherwise as will be apparent to one skilled inthe art. Further, variations on sub-propositions may be explored byusers adjusting sub-propositions within the chain of reasoning. Theinterrelationships and sub-propositions may be further determined ormodulated by domain-specific rules and the like. Furthermore, in someexamples, the interrelationships or truth values of propositions may beprobabilistic (e.g., likely or unlikely to be true rather than strictlytrue or untrue). A proposition being true or untrue may increase thelikelihood of another proposition being true or untrue. Thus, theconclusion tabulator 220 may provide multiple conclusions of varyinglikelihood based on the likelihoods of individual propositions and/orprobabilistic interrelationships between true or false propositions. Theresults of the conclusion tabulator 220 can also feed into otherservices, such as other conclusion tabulators (e.g., a recursive oriterative exploration of a chain of reasoning), reporting services, orother decision-making type services, and the like.

FIGS. 4 and 5 respectively depict an extended system 400 and method 500for processing multiple propositions. The system 400 can identifypropositions that may be implicitly imputed by a proposition orpropositions explicitly or specifically stated in a set of naturallanguage content. For example, a proposition describing a politician mayimply one or more suppositions underlying the proposition where thesuppositions are themselves propositions. Further, these implicitpropositions may themselves further include implicit propositions, andso the extended system 400 allows users to repeatedly drill down througha hierarchy of propositions and identify supporting propositions and/ornegating propositions.

A proposition taxonomy 402—which may include an NLPT such as thatdiscussed above in reference the NLPT 106—may receive one or moreinterrelated propositions from the proposition interrelationshipidentifier 218 (operation 502). Further, the proposition taxonomy 402may provide new proposition interrelationships to the propositioninterrelationship identifier 218.

In some examples, the proposition taxonomy 402 can provide the updatedpropositions to users via the user interface 208. Users can likewise usethe interface 208 to provide feedback or further information to theproposition taxonomy 402. For example, new propositions may be providedand directly placed within the NLPT used by the proposition taxonomy402, thus defining the interrelationships between the new propositionsand various other propositions previously already in the NLPT. Users mayalso remove propositions and/or update the interrelationships betweenpropositions.

The proposition taxonomy 402 determines the locations for each receivedproposition using a proposition taxonomy, such as the NLPT (operation504). The taxonomy itself can be of any construction containing ahierarchical organization. For example, graphs, trees, sets, lists, andthe like may be used by the proposition taxonomy 402.

In some examples, proposition locations within the proposition taxonomy402 can be displayed through the user interface 208 to one or moreusers. The displayed locations may be provided as a mapping of naturallanguage content to neutral language contained within the propositiontaxonomy 402 (e.g., neutral language contained within nodes of a NLPT ofthe proposition taxonomy 402 as discussed above) in order to review athird party argument in neutral terms, review the user's own argument inneutralized terms, or both. Further, in the case of a discourse,locations of each participant's language within the proposition taxonomy402 can be identified as to overlaps and divergences between the twoparticipants' mapped natural language content (e.g., their locationswithin the proposition taxonomy 402) can be provided to users throughthe user interface 208 in visual form (e.g., flow-charts, bands, bargraphs, Venn diagrams, and the like).

In another example, a series of interchanges, such as throughout adiscourse or argument between multiple parties, can be monitored by thesystem 400 to identify divergent term usage. For example, a propositiontaxonomy 402 may either explicitly or otherwise identifies a firstproposition as “all,” a first response to the first proposition as“most,” and a second response as “some.” The diverging propositions canbe identified, and users may be alerted in order to ensure allparticipants are “on the same page” or discussing the same concepts. Inother words, the system 400 may measure a vocabulary alignment betweenmultiple users. In some examples, a vocabulary alignment (or lackthereof) over a sequence of natural language content may identify adrift or shifting of propositions.

A proposition flow tree generator 404 can generate a proposition flowtree based on the proposition locations within the proposition taxonomy402 (operation 506). The generated flow tree may be of differentconstructions, depending on the data structure used to store theproposition taxonomy. Notwithstanding, the generated flow tree mayprovide a mapping of propositions through the proposition hierarchy. Insome cases, multiple flow trees may be generated based on factors asforking proposition interrelationships within the proposition taxonomy(e.g., as in the case of a graph where each proposition is representedby a node and connections between propositions are represented as edgesconnecting nodes).

The flow tree generated by the proposition flow tree generator 404 maybe provided to users via the user interface 208. In some cases, usersmay explore or interact with the proposition flow tree through the userinterface 208. For example, a user 210 may validate a proposition flowalong a particular branch of the flow tree or may update the flow treewith a new proposition flow. In some cases, new propositions may beexposed within the proposition taxonomy 402, which may then be furthermapped by the proposition flow tree generator 404.

FIG. 7 depicts a user view 700 that includes a visualization of theproposition flow tree. Here, the flow tree is visualized by a dropdowntab 704. Each line included in the tab 704 indicates a propositionwithin the proposition taxonomy 402. At the top of the tab 704, thetopmost proposition 705 is semantically broad, encompassing allpoliticians. In contrast, the last line 706 of the tab 704 is narroweror more specified, indicating a particular politician of a particulartype of a particular affiliation and/or exhibiting a particular quality.

Further, the user view 700 includes a display of the processed naturallanguage content 702. Here, the proposition flow along a particularbranch is displayed; however, multiple proposition flows can bedisplayed (not depicted). In some examples, multiple proposition flowsmay be displayed adjacent to each other or through a tabbed displaymenu. In some examples, the display may include a graphical elementdepicting proposition interrelationships.

Returning to FIG. 4, the proposition flow tree generator 404 may alsoprovide the proposition flow tree to a proposition conflicts detector406 in order to identify proposition conflicts based on the flow tree(operation 508). Conflict detection algorithms may vary based on thedata structure used by the proposition taxonomy 402. For example, acircuit within a directional graph may identify circular reasoning basedon the proposition interrelationships (e.g., edges between nodesrepresenting propositions). The proposition conflicts detector 406 mayreceive input from users through the user interface 208, and users maynotified by the propositions conflicts detector 406 with new conflicts.In such cases, the proposition taxonomy 402 may also be updated tofurther include user-identified proposition conflicts.

FIG. 9 depicts an argument analysis visualization 900 that displaysproposition conflicts associated with a tagged argument. The propositionconflicts detector 406, the argument type tagger 206, the propositiontaxonomy 402, or a combination of the three can provide the argument902, which includes the argument type tagged to the proposition underconsideration 802.

Particular propositional components of the argument type as applied maybe extracted and displayed for the user. Here, the “analogy ofattributes” argument tag has been identified and applied to thepropositions included in the argument 902. Because arguments by analogyof attributes rely on similar attributes, a column depicting properties(e.g., attributes) in common 904 between the analogized subjects can bejuxtaposed with a column depicting attributes that are different fromeach other. In some examples, these columns may be filled in by users210, the system curator 212, or the community 214. In other examples,the taxonomies specific to the propositions of the argument can be usedto fill in the column based on proposition interrelationships withineach taxonomy. Further, the columns may be automatically filled-in bythe system and can improve as more humans provide column entries (e.g.,such as propositions associated with entities identified in the argument902) upon which the system may train itself.

FIG. 10 depicts a visualization 1000 of a proposition flow tree providedto the user interface 208. The original natural language content 1002 isdisplayed in a scrollable column and includes proposition tags 1005 aswell as an argument tag 1008. Further, a conclusion button 1010 allows auser to manually activate the conclusion tabulator 220.

A comment tree section 1004 details the proposition flow tree associatedwith a selected proposition. Here, the selected proposition 1006 ishighlighted both within the natural language content 1002 and in thecomment tree section 1004, where it acts as the root node 1012 of thecomment tree. Propositions 1013A-B associated with the selectedproposition 1006 are listed below the root node 1012. Propositions1013A-B are associated with the root node 1012 based on their relativelocations within the proposition taxonomy 402.

Each depending proposition 1013A-B is evaluated as either a supportingproposition 1013A or a negating proposition 1013B. Furthermore,depending propositions 1013A-B may each include depending propositiontrees 1014 of their own. In turn, propositions included within dependingproposition trees 1014 may similarly include further dependingproposition trees 1016 of their own and so on. Depending propositiontrees 1014, 1016, and so on—in combination with the root node 1012 andits associated depending propositions 1013A-B—make up the propositionflow tree displayed in the comment tree 1004. Each proposition treeincludes a logic operator 1013C, which aggregates the propositions inthe tree and their interrelationships to determine a validity of theassociated root proposition.

In some examples, the logic operator 1013C may include a dependingproposition tree 1015 that itself includes a logic operator (which mayalso include a depending tree with a logic operator, and so on). Thedepending proposition tree 1015 may include pre-requisite premisesnecessary for successful tabulation by the logic operator 1013C. In someexamples, ontologies and other systems can identify the pre-requisitepremises. In other examples, a user (e.g., the creator of the naturallanguage content or a user reviewing another's natural language content)may be prompted to provide any pre-requisite premises necessary fortabulation.

In some examples, the comment tree 1004 can depict or otherwise flag aburden of proof related to particular propositions 1013A-B orpropositions included in the depending proposition trees 1014 and 1016.The burden of proof may include a measure of distance between aproposition and an opposed proposition within the proposition taxonomy402. Where an opposed proposition is less often indicated in naturallanguage content than the current proposition, for example, the burdenof proof (and a notice indicating it) may be shifted to advocates of theopposed proposition.

FIG. 6 depicts a method 600 that may be performed by the argumentationanalytics engine 110 to further provide argument analyses to the user112. The argumentation analytics engine 110 may identify agents andobjects of an argument (operation 602). Agents include the actors oractive participant in a causal argument. For example, an argumentespousing the negligence of a particular community may identifyparticular member of that community whose actions had a deleteriouseffect on others inside and outside the community. Those impacted may beidentified as objects of an argument. In some examples, the objects in acausal argument may become agents in a subsequent round of causaleffects (e.g., in response to the agent).

In some examples, the argumentation analytics engine 110 may identify anauthor of the content itself as the agent (e.g., an author opining aboutwhat he himself should do). In this case, the very act of writing mayinform latter operations of method 600. Further, the agent or thoseresponding to the content written by the agent may use the argumentationanalytics engine 110 to process the natural language content of theagent.

In some cases, intended audiences of the causal argument may beidentified as well. An argument can be intended to persuade agents,objects, third parties, or various combinations of the three. Forexample, an agent and/or object may be targeted by the argument forcastigation while third party opinions toward the agent and/or objectare intended to be lowered.

The argumentation analytics engine 110 may then identify the incentivesof the agents (operation 604). Incentives may be identifiedautomatically by the argumentation analytics engine 110 or may beprovided by the user 112. For example, the argumentation analyticsengine 110 may identify incentives based on an agent's behavior withinthe argument, based on the agent's historical behavior, or both.

The argumentation analytics engine 110 can identify core values of theagents based on the identified objects and incentives (operation 606).Further, the core values of the agents may denote value conflictsbetween the agents, the objects, and/or the audience. In some cases, avalue conflict may indicate a fundamental emotional trade-off of asituation and may be unresolvable. However, the identified valueconflict may provide an indication of propositions to avoid and/or seekbased on the intent of a party to an argument. In some examples, thecore values can include subjective valuations, such as aestheticopinions or preferences that are not premised upon sub-propositions or achain of reasoning and are thus irrefutable or axiomatic. Further, insome cases, an agent's use of the method 600 may itself inform theidentification of core values of that agent. In other examples, theargumentation analytics engine 110 may learn to identify the core valuesof an agent based on the agent's repeated interaction with the system.In other examples, the system itself may directly interact or query theagent (e.g., where the agent is also a generator of the natural languagecontent).

Further, the argumentation analytics engine 110 may identify componentsof persuasive leverage based on the identified objects, incentives, andcore values (operation 608). Components of persuasive leverage mayinclude anecdotes, evidence, and logical constructs which can beprovided directly to the user 112. In some examples, the components ofpersuasive leverage may be used to inform the neutral languageproposition taxonomy or applied to it as an added filter in order togenerate propositional taxonomies reflective of agent, object, oraudience-specific characteristics. The components of persuasive leveragemay be included as a data field or structure for other downstreamprocesses, such as training a system model or ontology, rendering to theuser, or for other processing and the like.

Further, the incentives, core values, or components of persuasiveleverage may individually or together inform the idea argumentationmapper 108 of relationships between propositions and the like. In otherexamples, where axiomatic core values are identified, the value can be aterminal endpoint within the neutral language proposition taxonomy. Inother words, because the proposition is presumed true by the agent, theproposition may itself only be a proposition for other propositions andwill not include sub-propositions informing it (e.g., during conclusiontabulation). Furthermore, the idea argumentation mapper 108 maysimilarly, or as a result, identify axiomatic core values as endpointsof a map of related arguments or ideas. In some examples, the agents,objects, incentives, values, audience, and the interrelationship betweenthem may be displayed as a state-machine. Transitions between states ofthe state-machine may map the kind and amount of influence between theagents, objects, audience, and respective incentives and values.Further, the agents, objects, audience, and respective incentives andvalues may be processed as data primitives, for example, stored within adata object for later access by the argumentation analytics engine 110.For example, the argumentation analytics engine 110 may update existingor generate new ontologies based on the data object and includedprimitives. In another example, users 210 may review primitives relatedto processed natural language content or may explore listings ofprimitives processed by the system.

FIG. 11 is an example computing system 1100 that may implement varioussystems and methods discussed herein. The computer system 1100 includesone or more computing components in communication via a bus 1102. In oneimplementation, the computing system 1100 includes one or moreprocessors 1104. The processor 1104 can include one or more internallevels of cache 1106 and a bus controller or bus interface unit todirect interaction with the bus 1102. The processor 1104 mayspecifically implement the various methods discussed herein. Main memory1108 may include one or more memory cards and a control circuit (notdepicted), or other forms of removable memory, and may store varioussoftware applications including computer executable instructions, thatwhen run on the processor 1104, implement the methods and systems setout herein. Other forms of memory, such as a storage device 1110 and amass storage device 1118, may also be included and accessible, by theprocessor (or processors) 1104 via the bus 1102. The storage device 1110and mass storage device 1118 can each contain any or all of the methodsand systems discussed herein.

The computer system 1100 can further include a communications interface1112 by way of which the computer system 1100 can connect to networksand receive data useful in executing the methods and system set outherein as well as transmitting information to other devices. Thecomputer system 1100 can also include an input device 1120 by whichinformation is input. Input device 1116 can be a scanner, keyboard,and/or other input devices as will be apparent to a person of ordinaryskill in the art. An output device 1114 can be a monitor, speaker,and/or other output devices as will be apparent to a person of ordinaryskill in the art.

The system set forth in FIG. 11 is but one possible example of acomputer system that may employ or be configured in accordance withaspects of the present disclosure. It will be appreciated that othernon-transitory tangible computer-readable storage media storingcomputer-executable instructions for implementing the presentlydisclosed technology on a computing system may be utilized.

In the present disclosure, the methods disclosed may be implemented assets of instructions or software readable by a device. Further, it isunderstood that the specific order or hierarchy of steps in the methodsdisclosed are instances of example approaches. Based upon designpreferences, it is understood that the specific order or hierarchy ofsteps in the methods can be rearranged while remaining within thedisclosed subject matter. The accompanying method claims presentelements of the various steps in a sample order, and are not necessarilymeant to be limited to the specific order or hierarchy presented.

The described disclosure may be provided as a computer program product,or software, that may include a computer-readable storage medium havingstored thereon instructions, which may be used to program a computersystem (or other electronic devices) to perform a process according tothe present disclosure. A computer-readable storage medium includes anymechanism for storing information in a form (e.g., software, processingapplication) readable by a computer. The computer-readable storagemedium may include, but is not limited to, optical storage medium (e.g.,CD-ROM), magneto-optical storage medium, read only memory (ROM), randomaccess memory (RAM), erasable programmable memory (e.g., EPROM andEEPROM), flash memory, or other types of medium suitable for storingelectronic instructions.

The description above includes example systems, methods, techniques,instruction sequences, and/or computer program products that embodytechniques of the present disclosure. However, it is understood that thedescribed disclosure may be practiced without these specific details.

While the present disclosure has been described with references tovarious implementations, it will be understood that theseimplementations are illustrative and that the scope of the disclosure isnot limited to them. Many variations, modifications, additions, andimprovements are possible. More generally, implementations in accordancewith the present disclosure have been described in the context ofparticular implementations. Functionality may be separated or combinedin blocks differently in various embodiments of the disclosure ordescribed with different terminology. These and other variations,modifications, additions, and improvements may fall within the scope ofthe disclosure as defined in the claims that follow.

What is claimed is:
 1. A method for processing natural languagearguments, the method comprising: receiving a set of natural languageinput; associating a proposition tag with a first portion of the naturallanguage input identified as including a first proposition, theproposition tag linking the first portion of the natural language inputto an emotionally neutral proposition value; associating a rhetoricalargument type with a second portion of the natural language input, theassociated rhetorical argument type identified based on rules fordetermining an amount of support for an argument that a propositionprovides; tabulating a conclusion based on the first proposition and theidentified rhetorical argument type; and generating a map of the set ofnatural language input for display, wherein the map visually illustratesthat the first portion of the natural language input associated with theproposition tag and that the second portion of the natural languageinput is associated with the identified rhetorical argument type.
 2. Themethod of claim 1, further comprising: associating a second propositiontag with a third portion of the natural language input identified asincluding a second proposition, the second proposition tag linking thethird portion of the natural language input to a second emotionallyneutral proposition value; organizing the first proposition and thesecond proposition based on the identified rhetorical argument type; andidentifying an interrelationship between the first proposition and thesecond proposition.
 3. The method of claim 1, further comprisinglocating the first proposition within a proposition taxonomy, the firstproposition taxonomy comprising a plurality of hierarchically-organizedpropositions, wherein the map is a proposition flow tree comprising aneighbor proposition and the first proposition, the neighbor propositionadjacent to the first proposition within the proposition taxonomy. 4.The method of claim 3, further comprising identifying aninterrelationship between the first proposition and the neighborproposition, the interrelationship reflecting an effect that alikelihood of truth of the neighbor proposition has on a likelihood oftruth of the first proposition.
 5. The method of claim 3, furthercomprising identifying a proposition conflict based on the propositionflow tree, the proposition conflict comprising two or more incompatiblepropositions within the proposition flow tree.
 6. The method of claim 5,further comprising evaluating the two or more incompatible propositionsto identify a respective likelihood of truth.
 7. The method of claim 1,further comprising: identifying an agent of the natural language inputand an object of the natural language input, the agent impacting theobject; identifying an incentive of the agent; determining a core valueof the agent based on the incentive and the object; and generating acomponent of persuasive leverage based on one of the core value, theincentive, and the object.
 8. The method of claim 1, wherein the mapfurther includes at least one flag indicating a burden of proofassociated with the first proposition.
 9. The method of claim 1, whereinthe rhetorical argument type corresponds to a rhetorical principle, andwherein the rhetorical argument type is based on whether the rhetoricalprinciple is violated by the argument in relation to the proposition.10. The method of claim 9, wherein the rhetorical principle isassociated with at least one of fact being contradicted, argument byanalogy, category mismatch, solution being proposed, consequence beingasserted, outcome different from intent, topic shift or splitting,non-sequitur, questions of degree, confusion of subject agency oraffected parties, assignment of burden of proof, question begging, orincompleteness of a set.
 11. A system for processing natural languagearguments, the system comprising: a user interface that receives a setof natural language input; a processor that executes instructions storedin memory, wherein execution of the instructions by the processor:associates a proposition tag with a first portion of the naturallanguage input identified as including a first proposition, theproposition tag linking the first portion of the natural language inputto an emotionally neutral proposition value; associates a rhetoricalargument type with a second portion of the natural language input, theassociated rhetorical argument type identified based on rules fordetermining an amount of support for an argument that a propositionprovides; tabulates a conclusion based on the first proposition and theidentified rhetorical argument type; and generates a map of the set ofnatural language input for display, wherein the map visually illustratesthat the first portion of the natural language input associated with theproposition tag and that the second portion of the natural languageinput is associated with the identified rhetorical argument type; and adisplay screen that displays the generated map.
 12. The system of claim11, wherein the processor executes further instructions to: associate asecond proposition tag with a third portion of the natural languageinput identified as including a second proposition, the secondproposition tag linking the third portion of the natural language inputto a second emotionally neutral proposition value; organize the firstproposition and the second proposition based on the identifiedrhetorical argument type; and identify an interrelationship between thefirst proposition and the second proposition.
 13. The system of claim11, wherein the processor executes further instructions to locate thefirst proposition within a proposition taxonomy, the first propositiontaxonomy comprising a plurality of hierarchically-organizedpropositions, wherein the map is a proposition flow tree comprising aneighbor proposition and the first proposition, the neighbor propositionadjacent to the proposition within the proposition taxonomy.
 14. Thesystem of claim 13, wherein the processor executes further instructionsto identify an interrelationship between the first proposition and theneighbor proposition, the interrelationship reflecting an effect that alikelihood of truth of the neighbor proposition has on a likelihood oftruth of the first proposition.
 15. The system of claim 13, wherein theprocessor executes further instructions to identify a propositionconflict based on the proposition flow tree, the proposition conflictcomprising two or more incompatible propositions within the propositionflow tree.
 16. The system of claim 15, wherein the processor executesfurther instructions to evaluate the two or more incompatiblepropositions to identify a respective likelihood of truth.
 17. Thesystem of claim 11, wherein the processor executes further instructionsto: identify an agent of the natural language input and an object of thenatural language input, the agent impacting the object; identify anincentive of the agent; determine a core value of the agent based on theincentive and the object; and generate a component of persuasiveleverage based on one of the core value, the incentive, and the object.18. The system of claim 11, wherein the map further includes at leastone flag indicating a burden of proof associated with the firstproposition.
 19. A non-transitory computer-readable storage medium,having embodied thereon a program executable by a processor to perform amethod for processing natural language arguments, the method comprising:receiving a set of natural language input; associating a proposition tagwith a first portion of the natural language input identified asincluding a first proposition, the proposition tag linking the firstportion of the natural language input to an emotionally neutralproposition value; associating a rhetorical argument type with a secondportion of the natural language input, the associated rhetoricalargument type identified based on rules for determining an amount ofsupport for an argument that a proposition provides; tabulating aconclusion based on the first proposition and the identified rhetoricalargument type; and generating a map of the set of natural language inputfor display, wherein the map visually illustrates that the first portionof the natural language input associated with the proposition tag andthat the second portion of the natural language input is associated withthe identified rhetorical argument type.